Multi-dimensional database design and implementation of dam safety monitoring system

To improve the effectiveness of dam safety monitoring database systems, the development process of a multi-dimensional conceptual data model was analyzed and a logic design wasachieved in multi-dimensional database mode. The optimal data model was confirmed by identifying data objects, defining relations and reviewing entities. The conversion of relations among entities to external keys and entities and physical attributes to tables and fields was interpreted completely. On this basis, a multi-dimensional database that reflects the management and analysis of a dam safety monitoring system on monitoring data information has been established, for which factual tables and dimensional tables have been designed. Finally, based on service design and user interface design, the dam safety monitoring system has been developed with Delphi as the development tool. This development project shows that the multi-dimensional database can simplify the development process and minimize hidden dangers in the database structure design. It is superior to other dam safety monitoring system development models and can provide a new research direction for system developers.

Goodness-of-fit tests for multi-dimensional copulas:Expanding application to historical drought data

The question of how to choose a copula model that best fits a given dataset is a predominant limitation of the copula approach, and the present study aims to investigate the techniques of goodness-of-fit tests for multi-dimensional copulas. A goodness-of-fit test based on Rosenblatt＇s transformation was mathematically expanded from two dimensions to three dimensions and procedures of a bootstrap version of the test were provided. Through stochastic copula simulation, an empirical application of historical drought data at the Lintong Gauge Station shows that the goodness-of-fit tests perform well, revealing that both trivariate Gaussian and Student t copulas are acceptable for modeling the dependence structures of the observed drought duration, severity, and peak. The goodness-of-fit tests for multi-dimensional copulas can provide further support and help a lot in the potential applications of a wider range of copulas to describe the associations of correlated hydrological variables. However, for the application of copulas with the number of dimensions larger than three, more complicated computational efforts as well as exploration and parameterization of corresponding copulas are required.

Data inversion of multi-dimensional magnetic resonance in porous media

Since its inception in the 1970s,multi-dimensional magnetic resonance(MR)has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions.MR spectroscopy beyond one dimension allows the study of the correlation,exchange processes,and separation of overlapping spectral information.The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media.Apart from Fourier transform,methods have been developed for processing the multi-dimensional time-domain data,identifying the fluid components,and estimating pore surface permeability via joint relaxation and diffusion spectra.Through the resolution of spectroscopic signals with spatial encoding gradients,multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases.Signals in each voxel are usually expressed as multi-exponential decay,representing microstructures or environments along multiple pore scales.The separation of contributions from different environments is a common ill-posed problem,which can be resolved numerically.Moreover,the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra.This paper reviews the algorithms that have been proposed to process multidimensional MR datasets in different scenarios.Detailed information at the microscopic level,such as tissue components,fluid types and food structures in multi-disciplinary sciences,could be revealed through multi-dimensional MR.

Finding Main Causes of Elevator Accidents via Multi-Dimensional Association Rule in Edge Computing Environment

In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.

Design of similarity measure for discrete data and application to multi-dimension

Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and distance measure, and were proved. To calculate the degree of similarity of discrete data, relative degree between data and total distribution was obtained. Discrete data similarity measure was completed with combination of mentioned relative degrees. Power interconnected system with multi characteristics was considered to apply discrete similarity measure. Naturally, similarity measure was extended to multi-dimensional similarity measure case, and applied to bus clustering problem.

Outlier detection based on multi-dimensional clustering and local density

Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outlier. In this work, an effective outlier detection method based on multi-dimensional clustering and local density(ODBMCLD) is proposed. ODBMCLD firstly identifies the center objects by the local density peak of data objects, and clusters the whole dataset based on the center objects. Then, outlier objects belonging to different clusters will be marked as candidates of abnormal data. Finally, the top N points among these abnormal candidates are chosen as final anomaly objects with high outlier factors. The feasibility and effectiveness of the method are verified by experiments.

Multi-dimension and multi-modal rolling mill vibration prediction model based on multi-level network fusion

Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.

Visual exploration of multi-dimensional data via rule-based sample embedding

We propose an approach to learning sample embedding for analyzing multi-dimensional datasets.The basic idea is to extract rules from the given dataset and learn the embedding for each sample based on the rules it satisfies.The approach can filter out pattern-irrelevant attributes,leading to significant visual structures of samples satisfying the same rules in the projection.In addition,analysts can understand a visual structure based on the rules that the involved samples satisfy,which improves the projection’s pattern interpretability.Our research involves two methods for achieving and applying the approach.First,we give a method to learn rule-based embedding for each sample.Second,we integrate the method into a system to achieve an analytical workflow.Cases on real-world dataset and quantitative experiment results show the usability and effectiveness of our approach.

Painting image browser applying an associate-rule-aware multidimensional data visualization technique

Exploration of artworks is enjoyable but often time consuming.For example,it is not always easy to discover the favorite types of unknown painting works.It is not also always easy to explore unpopular painting works which looks similar to painting works created by famous artists.This paper presents a painting image browser which assists the explorative discovery of user-interested painting works.The presented browser applies a new multidimensional data visualization technique that highlights particular ranges of particular numeric values based on association rules to suggest cues to find favorite painting images.This study assumes a large number of painting images are provided where categorical information(e.g.,names of artists,created year)is assigned to the images.The presented system firstly calculates the feature values of the images as a preprocessing step.Then the browser visualizes the multidimensional feature values as a heatmap and highlights association rules discovered from the relationships between the feature values and categorical information.This mechanism enables users to explore favorite painting images or painting images that look similar to famous painting works.Our case study and user evaluation demonstrates the effectiveness of the presented image browser.

Multidimensional Data Querying on Tree-Structured Overlay

Multidimensional data query has been gaining much interest in database research communities in recent years, yet many of the existing studies focus mainly on ten tralized systems. A solution to querying in Peer-to-Peer（P2P） environment was proposed to achieve both low processing cost in terms of the number of peers accessed and search messages and balanced query loads among peers. The system is based on a balanced tree structured P2P network. By partitioning the query space intelligently, the amount of query forwarding is effectively controlled, and the number of peers involved and search messages are also limited. Dynamic load balancing can be achieved during space partitioning and query resolving. Extensive experiments confirm the effectiveness and scalability of our algorithms on P2P networks.

FAAD:an unsupervised fast and accurate anomaly detection method for a multi-dimensional sequence over data stream

Recently, sequence anomaly detection has been widely used in many fields. Sequence data in these fields are usually multi-dimensional over the data stream. It is a challenge to design an anomaly detection method for a multi-dimensional sequence over the data stream to satisfy the requirements of accuracy and high speed. It is because:(1) Redundant dimensions in sequence data and large state space lead to a poor ability for sequence modeling;(2) Anomaly detection cannot adapt to the high-speed nature of the data stream, especially when concept drift occurs, and it will reduce the detection rate. On one hand, most existing methods of sequence anomaly detection focus on the single-dimension sequence. On the other hand, some studies concerning multi-dimensional sequence concentrate mainly on the static database rather than the data stream. To improve the performance of anomaly detection for a multi-dimensional sequence over the data stream, we propose a novel unsupervised fast and accurate anomaly detection(FAAD) method which includes three algorithms. First, a method called "information calculation and minimum spanning tree cluster" is adopted to reduce redundant dimensions. Second, to speed up model construction and ensure the detection rate for the sequence over the data stream, we propose a method called"random sampling and subsequence partitioning based on the index probabilistic suffix tree." Last, the method called "anomaly buffer based on model dynamic adjustment" dramatically reduces the effects of concept drift in the data stream. FAAD is implemented on the streaming platform Storm to detect multi-dimensional log audit data.Compared with the existing anomaly detection methods, FAAD has a good performance in detection rate and speed without being affected by concept drift.

EPMDA:an efficient privacy-preserving multi-dimensional data aggregation scheme for edge computing-based IoT system

In order to perform multi-dimensional data aggregation operations efficiently in edge computing-based Internet of things(IoT) systems, a new efficient privacy-preserving multi-dimensional data aggregation(EPMDA) scheme is proposed in this paper. EPMDA scheme is characterized by employing the homomorphic Paillier encryption and SM9 signature algorithm. To improve the computation efficiency of the Paillier encryption operation, EPMDA scheme generates a pre-computed modular exponentiation table of each dimensional data, and the Paillier encryption operation can be implemented by using only several modular multiplications. For the multi-dimensional data, the scheme concatenates zeros between two adjacent dimensional data to avoid data overflow in the sum operation of ciphertexts. To enhance security, EPMDA scheme sets random number at the high address of the exponent. Moreover, the scheme utilizes SM9 signature scheme to guarantee device authentication and data integrity. The performance evaluation and comparison show that EPMDA scheme is more efficient than the existing multi-dimensional data aggregation schemes.

泰森多边形在地质数据去丛聚中的应用

钻井总是被优先设计在有利储层的部位,导致获取的地质数据分布不均匀。为了使地质数据的统计结果更客观地反映实际情况,需要对地质数据进行去丛聚处理,其核心思想就是给密集的数据赋较小的权值,给稀疏的数据赋较大的权值。笔者用泰森多边形法求取钻井的控制范围,以此确定该井数据统计时的权值大小,为多边形去丛聚法中多边形的确定提供了新思路。应用本方法对石南油田某井区的孔隙度分布进行了分析,验证了该方法的可行性。

并行数据库的查询处理并行化技术和物理设计方法

近几年，随着并行计算机系统的迅速发展，并行数据库系统已经成为一个新的数据库研究领域，引起了学术界和工业界的极大关注，很多研究成果已经出现．本文是综述并行数据库系统研究与进展情况的两篇文章之一，重点探讨目前并行数据库系统的研究方向和问题，综述有关并行数据库的物理设计方法和查询处理并行化技术的主要研究成果．

并行数据库上的并行CMD-Join算法

并行数据库在多处理机之间的分布方法（简称数据分布方法）对并行数据操作算法的性能影响很大．如果在设计并行数据操作算法时充分利用数据分布方法的特点，可以得到十分有效的并行算法．本文研究如何充分利用数据分布方法的特点，设计并行数据操作算法的问题，提出了基于ＣＭＤ多维数据分布方法的并行ＣＭＤ－Ｊｏｉｎ算法．理论分析和实验结果表明。

一种并行数据库的动态多维数据分布方法

并行数据库系统的性能与数据库在多处理机之间的分布密切相关．目前已经出现一些并行数据库的数据分布方法．但是，这些方法都不能有效地支持动态数据库．文章提出了一种并行数据库的动态多维数据分布方法．该方法不仅能够有效地支持动态数据库的分布，还具有多维数据分布的诸多优点．此方法由初始数据分布机构和启发式动态数据分布调整机构组成．初始分布机构完成给定数据库文件的初始分布．动态数据分布调整机构实现动态数据库数据分布的动态调整．理论分析和实验结果表明，这种方法十分有效。

网络RAID布局研究

1 背景随着大容量存储系统需求的不断增加,RAID技术得到了越来越广泛的应用。对于使用简单的奇偶校验(RAID5)的RAID系统,平均无故障时间与磁盘数和修复时间成反比,因此当系统规模较大时,可靠性就会大大降低。为解决此问题,Muntz 和Lui提出了校验散布(parity declustering)思想:设置条纹大小小于磁盘总数,将重构负载均匀分布到所有磁盘,这就降低了故障状态下单个磁盘负载增加的比率,提高了重构性能,减少了重构时间,从而提高了阵列的可靠性。Holland和Gibson对校验散布思想进行了较为系统的研究,提出了理想布局的6条标准和采用不完全区组设计(BIBD)的布局构造方法。之后此领域的研究工作集中于设计更优的布局构造方法,主要成果有随机排列法,基于环的BIBD,DATUM,PRIME,RELPR,PDDL等。

基于Cluster结构的多维动态数据分布方法

数据分布是数据库查询并行处理的基础,良好的数据分布方法对查询性能有着重要影响.本文提出了一种新的基于Cluster结构的多维动态数据分布方法,该方法能保证数据均匀分布在多个处理机上;能动态调整数据片段的大小,使关系始终保持最优并行度;并能有效地支持各属性上的查询操作.性能分析及实验结果表明,在大规模的并行系统中,本文方法的性能优于过去的数据分布方法.

并行I/O中大型多维数据集合分配策略研究

在大型多维数据集合处理中,对多维数据集合的拆分及其在磁盘上的存储分配是重要的研究课题。由于磁盘的机械运动已形成了数据I/O时的速度瓶颈,因此通过采用并行I/O技术,将多维数据进行有效的拆分,并在多个磁盘间进行分布存储是克服瓶颈的有效办法。基于此,论文中提出了一种多维数据的循环拆分方法,它是对二维数据集合循环拆分分配方法的扩展,性能比较与分析表明了新算法的有效性。

分块同步磁盘阵列数据存取时间近似计算

本文提出了一种有效的计算分块同步磁盘阵列数据存取时间的近似方法。分析表明，要获得较好的近似结果，应将分块度限制在２０以内。